Molecular representation learning: cross-domain foundations and future Frontiers

This review surveys trends in molecular representation learning—including GNNs, VAEs, transformers, hybrid SSL models—and their roles in property prediction, generative modeling, and cross-domain generalization.

Authors

Rahul Sheshanarayana (ORCID: https://orcid.org/0000-0002-6651-0519)
Fengqi You (ORCID: https://orcid.org/0000-0001-9609-4299)

Institutions

Cornell University (US)

Publication Details

Journal: Digital Discovery
Published: 2025-01-01
DOI: https://doi.org/10.1039/d5dd00170f
Citations: 2
Primary Topic: Computational Drug Discovery Methods
Type: article
Field-Weighted Citation Impact: 3.01

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article

Molecular representation learning: cross-domain foundations and future Frontiers

Rahul Sheshanarayana, Fengqi You

Jan 1, 2025

2 citations

Digital Discovery

Computational Drug Discovery Methods

3.01

article

Molecular representation learning: cross-domain foundations and future Frontiers

Rahul Sheshanarayana, Fengqi You

January 1, 2025article en

2 citations

Abstract

Digital DiscoveryVol. 4(9)

Cornell University (US)

Openalex Percentile: Top 8%

Computational Drug Discovery Methods

3.01

DOI Open Access PDF OpenAlex

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Molecular representation learning: cross-domain foundations and future Frontiers — Rahul Sheshanarayana, Fengqi You · Digital Discovery (2025) | TGRS Research Map | TGRS